Increased M&A Activity in the AI Space: PitchBook Report Recap

According to Pitchbook, recent data shows a sharp increase in the number of strategic acquisition deals from major tech giants of small tactical AI companies. There are multiple reasons why a giant would want to mass acquire startups, the primary of which is adding capital whether that comes in the form of a solid working team with subject matter expertise or in the form of assets held by the target. 

More nuanced than that, many acquisitions come from an offensive play in the market to take a small target company off the market to prevent competitors from entering a new space. Especially with AI, which in regulatory terms is quite new, companies can feel free to play around with the market with little to no consequence. The M&A activity reflects the mindset of these giants to onboard new teams rather than develop their pre-existing teams to be able to handle new issues. 

Major acquirers have been increasing activity in 3 specific areas of AI tech: Core AI, Consumer AI, and National Language Technology. The distribution of acquisitions across these spaces is dependent mostly on the target’s compatibility. For example, Apple invests more heavily in Consumer AI than other companies in FAMGA. The primary reason for this is Apple moving more closely to analyzing macro data for machine learning in the context of creating new applications for their consumers. 

As an investment strategy, a firm could almost develop an arbitrage strategy given a specific acquirer in mind. For example, if a firm decides that Apple is collecting a lot more consumer data than they are able to efficiently analyze and turn into actionable products, the firm should decide to invest in multiple consumers AI startups with the hope that Apple will acquire the startup. 

The Nature of AI Acquisitions

The nature of AI data analytics is uniquely strategic in terms of acquisitions. To elaborate, AI is meant to quickly analyze mass amounts of unstructured data without massive data cleaning, organization, or formatting. As such, it makes for the perfect time-saving acquisition that most FAMGA type organizations look for. 

These companies have the technical and human resources to create these types of products but just out of the cost heavy nature of product management, agile development, and computer scientist’s salaries, it often makes more sense to acquire smaller startups who are able to save time and capital in a massive way. 

Thinking about it from a business perspective, Apple’s acquisition of a consumer AI company may yield 2-3 industry differentiating features that in turn increases sales. To a mega-giant like Apple, their product is so popular in the market that to acquire even a sustainable 1% growth in sales is so significant that it is worth many millions of dollars of acquisitions. 

As investors, it is important to keep in mind that many of these companies are not solely looking for time-saving targets. Antitrust laws haven’t been updated well across tech spaces especially due to the fact that Tech is a sector that moves a lot quicker than legislation does. Antitrust laws still apply in large to Major tech companies due to the fact that many of them formed as groups of hundreds of acquisitions over the course of the decade.

Big Tech has been known for making large-scale acquisitions in order to enhance their product or to take out the competition. The largest acquisitions of this nature have come from the autonomous vehicle space and the semiconductor space. 

Understanding that AI technology may be a new space with large-scale acquisitions could be an exciting prospect for private Equity investors as it allows them to differentiate their current portfolios, and add significantly desirable Investments for their investors.

Shaping Future Deals

The use of AI technology is a break from traditional private equity investment strategies in that many of these companies are not majorly profitable, have large-scale operational capabilities that need optimization, and do not necessarily have a very obvious or multitudinous payout strategy. 

This makes it difficult for private equity firms to use old models to value AI companies. This balance can be struck best by analyzing AI companies through the lens of Industry comparables instead of evaluating based on EBITDA or revenue multiples.

Overall, the market is trending towards using more AI technology as the mass amount of data analytics and regulatory inefficiencies come from this space. This creates an exciting addition to most PE portfolios because PE firms are now going to be able to utilize many new AI Technologies and their other Investments. 

Process optimization is very common and simple as an AI Tech and would help many other non-technical Investments increase their productivity and in turn, their valuation.

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